Work detail

Extending Hotelling’s location model into Agent-based domain

Author: Bc. Jan Vainer
Year: 2018 - summer
Leaders: PhDr. Jiří Kukačka Ph.D.
Consultants:
Work type: Bachelors
Language: English
Pages: 64
Awards and prizes: B.A. with distinction from the Director of IES FSV UK for an extraordinarily good bachelors diploma thesis.
Deloitte Outstanding Thesis Award


Link: https://is.cuni.cz/webapps/zzp/detail/191798/
Abstract: This thesis examines behaviour of adaptive agents in Hotelling’s location model.
We conduct an agent-based simulation in Hotelling’s setting with two agents,
where the agents use Nash-Q learning mechanism for adaptation. Traditional
game-theoretic models often stand on strong assumptions imposed on players
such as rationality and perfect information. We explore what alternations or refinements
of results this technique brings in comparison to the original analytical
solution of the theoretical Hotelling’s location model.
We discover that under Nash-Q learning and quadratic consumer cost function,
agents with high enough valuation of future profits learn behaviour similar
to aggressive market strategy, where both agents make similar products and lead
a price war in order to eliminate their opponent from the market. This behaviour
closely resembles the Minimum differentiation principle from the original
Hotelling’s paper with linear consumer costs. This result is surprising because in
our simulation, quadratic consumer cost functions are used, which should result
in maximum differentiation of the products. Our results suggest that the Principle
of minimum differentiation could be justified based on repeated interaction
of the agents and long-run optimization.
Additionally, suitability of Reinforcement learning methods for use in agentbased
simulations with economic context is evaluated and advantages and disadvantages
of Nash-Q learning are discussed.

Partners

Deloitte

Sponsors

CRIF
McKinsey
Patria Finance